No abstract

No abstract

Background: We aim to develop an explainable deep learning (DL) network for the prediction of all-cause mortality directly from positron emission tomography myocardial perfusion imaging flow and perfusion polar map data and evaluate it using prospective testing. Methods: A total of 4735 consecutive patients referred for stress and rest 82 Rb positron emission tomography between 2010 and 2018 were followed up for all-cause mortality for 4.15 (2.24–6.3) years. DL network utilized polar maps of stress and rest perfusion, myocardial blood flow, myocardial flow reserve, and spill-over fraction combined with cardiac volumes, singular indices, and sex. Patients scanned from 2010 to 2016 were used for training and validation. The network was tested in a set of 1135 patients scanned from 2017 to 2018 to simulate prospective clinical implementation. Results: In prospective testing, the area under the receiver operating characteristic curve for all-cause mortality prediction by DL (0.82 [95% CI, 0.77–0.86]) was higher than ischemia (0.60 [95% CI, 0.54–0.66]; P <0.001), myocardial flow reserve (0.70 [95% CI, 0.64–0.76], P <0.001) or a comprehensive logistic regression model (0.75 [95% CI, 0.69–0.80], P <0.05). The highest quartile of patients by DL had an annual all-cause mortality rate of 11.87% and had a 16.8 ([95% CI, 6.12%–46.3%]; P <0.001)-fold increase in the risk of death compared with the lowest quartile patients. DL showed a 21.6% overall reclassification improvement as compared with established measures of ischemia. Conclusions: The DL model trained directly on polar maps allows improved patient risk stratification in comparison with established methods for positron emission tomography flow or perfusion assessments.

Low-dose ungated CT attenuation correction (CTAC) scans are commonly obtained with SPECT/CT myocardial perfusion imaging. Despite the characteristically low image quality of CTAC, deep learning (DL) can potentially quantify coronary artery calcium (CAC) from these scans in an automatic manner. We evaluated CAC quantification derived with a DL model, including correlation with expert annotations and associations with major adverse cardiovascular events (MACE). Methods: We trained a convolutional long short-term memory DL model to automatically quantify CAC on CTAC scans using 6,608 studies (2 centers) and evaluated the model in an external cohort of patients without known coronary artery disease (n 5 2,271) obtained in a separate center. We assessed agreement between DL and expert annotated CAC scores. We also assessed associations between MACE (death, revascularization, myocardial infarction, or unstable angina) and CAC categories (0, 1-100, 101-400, or .400) for scores manually derived by experienced readers and scores obtained fully automatically by DL using multivariable Cox models (adjusted for age, sex, past medical history, perfusion, and ejection fraction) and net reclassification index. Results: In the external testing population, DL CAC was 0 in 908 patients (40.0%), 1-100 in 596 (26.2%), 100-400 in 354 (15.6%), and .400 in 413 (18.2%). Agreement in CAC category by DL CAC and expert annotation was excellent (linear weighted k, 0.80), but DL CAC was obtained automatically in less than 2 s compared with about 2.5 min for expert CAC. DL CAC category was an independent risk factor for MACE with hazard ratios in comparison to a CAC of zero:

No abstract

No abstract

Objectives: We aimed to assess the differences in the severity and chest-computed tomography (CT) radio-morphological signs of SARS-CoV-2 B.1.1.7 and non-B.1.1.7 variants. Methods: We collected clinical data of consecutive patients with laboratory-confirmed COVID-19 and chest-CT imaging who were admitted to the Emergency Department between first September – 13th November 2020 (non-B.1.1.7 cases) and first March – 18th March 2021 (B.1.1.7 cases). We also examined the differences in the severity and radio-morphological features associated with COVID-19 pneumonia. Total pneumonia burden (%), mean attenuation of ground-glass opacities (GGO) and consolidation were quantified using deep-learning research software. Results: The final population comprised 500 B.1.1.7 and 500 non-B.1.1.7 cases. Patients with B.1.1.7 infection were younger (58.5 ± 15.6 vs 64.8 ± 17.3; p < .001) and had less comorbidities. Total pneumonia burden was higher in the B.1.1.7 patient group (16.1% [IQR:6.0–34.2%] vs 6.6% [IQR:1.2–18.3%]; p < .001). In the age-specific analysis, in patients < 60 years B.1.1.7 pneumonia had increased consolidation burden (0.1% [IQR:0.0–0.7%] vs 0.1% [IQR:0.0–0.2%]; p < .001), and severe COVID-19 was more prevalent (11.5% vs  4.9%; p = .032). Mortality rate was similar in all age groups. Conclusions: Despite B.1.1.7 patients were younger and had fewer comorbidities, they experienced more severe disease than non-B.1.1.7 patients, however the risk of death was the same between the two groups. Advances in knowledge: Our study provides data on deep-learning based quantitative lung lesion burden and clinical outcomes of patients infected by B.1.1.7 VOC. Our findings might serve as a model for later investigations, as new variants are emerging across the globe.

In both the telecommunication industry and academia, massive Multiple-Input Multiple-Output (MIMO) is a highly developed technology in wideband wireless communication system's prospects that incite widespread concentration. In the massive MIMO system, pilot contamination is considered a basic problem. The modeling of wireless systems possesses an important problem over system throughput. Even though, environmental security, and energy-saving, considered predictable developments and universal demands. Therefore, by representing all these results, this work aims to develop a novel billiards-inspired optimization technique to choose the best transmit antennas chosen by taking into consideration of multi-objective issue which increases both relative energy effectiveness and capability. Actually, the developed method optimally tunes a number of transmit antennas and decides which antenna to be chosen. At last, proposed work performance is evaluated and shown with existing techniques with respect to the relative efficiency analysis, capacity analysis, and optimal antenna selection analysis.